Notebook: Covid Data Visualization

Importing modules

Task 1:

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt 
print('modules are imported')

import warnings
warnings.filterwarnings('ignore')

modules are imported

Task 1.1:

Loading the Dataset

dataset_url = 'https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv'
df = pd.read_csv(dataset_url)

Task 1.2:

let's check the dataframe

df.head()

	Date	Country	Confirmed	Recovered	Deaths
0	2020-01-22	Afghanistan	0	0	0
1	2020-01-23	Afghanistan	0	0	0
2	2020-01-24	Afghanistan	0	0	0
3	2020-01-25	Afghanistan	0	0	0
4	2020-01-26	Afghanistan	0	0	0

df.tail()

	Date	Country	Confirmed	Recovered	Deaths
135823	2021-12-10	Zimbabwe	155817	0	4723
135824	2021-12-11	Zimbabwe	165002	0	4735
135825	2021-12-12	Zimbabwe	167140	0	4738
135826	2021-12-13	Zimbabwe	167140	0	4738
135827	2021-12-14	Zimbabwe	172012	0	4740

let's check the shape of the dataframe

df.shape

(135828, 5)

Task 2.1 :

let's do some preprocessing

What do you want to do here is to have data from the datethat the first COVID19 confirmed case has been recorded in each of the countries. For doing that, I have to get all of the rows that the confirmed column is more than zero.

df = df[df.Confirmed > 0] #filter data for confirmed covid cases > 0

you can see, I have the data related to each off the countries from the first date that the first confirmed cases has been recorded in those countries.

df.head()

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0

let's see data related to a country for example Italy

df[df.Country == "Italy"] #filter data for Italy

	Date	Country	Confirmed	Recovered	Deaths
58914	2020-01-31	Italy	2	0	0
58915	2020-02-01	Italy	2	0	0
58916	2020-02-02	Italy	2	0	0
58917	2020-02-03	Italy	2	0	0
58918	2020-02-04	Italy	2	0	0
...	...	...	...	...	...
59593	2021-12-10	Italy	5185270	0	134669
59594	2021-12-11	Italy	5206305	0	134765
59595	2021-12-12	Italy	5225517	0	134831
59596	2021-12-13	Italy	5238221	0	134929
59597	2021-12-14	Italy	5258886	0	135049

684 rows × 5 columns

df[df.Country == "Afghanistan"] #filter data for Afghanistan

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0
...	...	...	...	...	...
688	2021-12-10	Afghanistan	157858	0	7322
689	2021-12-11	Afghanistan	157858	0	7325
690	2021-12-12	Afghanistan	157858	0	7328
691	2021-12-13	Afghanistan	157648	0	7328
692	2021-12-14	Afghanistan	157660	0	7329

660 rows × 5 columns

let's see Global spread of Covid19

fig = px.choropleth(df, locations = "Country", locationmode = "country names", color = "Confirmed",
                    animation_frame = "Date")
fig.update_layout(title_text = "Global Spread of COVID-19 ")
fig.show()

Task 2.2 : Exercise

Let's see Global deaths of Covid19

• A good measure for that is the maximum infection rate or, in other words, maximum number of new infected cases over 24 hours.

df_china = df[df.Country == "China"]
df_china.head()

	Date	Country	Confirmed	Recovered	Deaths
24948	2020-01-22	China	548	28	17
24949	2020-01-23	China	643	30	18
24950	2020-01-24	China	920	36	26
24951	2020-01-25	China	1406	39	42
24952	2020-01-26	China	2075	49	56

df_china = df_china[["Date", "Confirmed"]]
df_china.head()

	Date	Confirmed
24948	2020-01-22	548
24949	2020-01-23	643
24950	2020-01-24	920
24951	2020-01-25	1406
24952	2020-01-26	2075

Task 3.1:

Let's Visualize how intensive the Covid19 Transmission has been in each of the country

let's start with an example:

Rate of infection = Calculate first derivative of "Confirmed" column

df_china["Infection Rate"] = df_china["Confirmed"].diff()
df_china.head()

	Date	Confirmed	Infection Rate
24948	2020-01-22	548	NaN
24949	2020-01-23	643	95.0
24950	2020-01-24	920	277.0
24951	2020-01-25	1406	486.0
24952	2020-01-26	2075	669.0

#line chart
px.line(df_china, x = "Date", y =["Confirmed","Infection Rate"])

df_china["Infection Rate"].max()

15136.0

Task 3.2:

Let's Calculate Maximum infection rate for all of the countries

df.head()

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0

countries = list(df["Country"].unique())
max_infection_rate = [] 
for c in countries:
    #MIR is max_infection_rate
    MIR = df[df["Country"]== c].Confirmed.diff().max()
    max_infection_rate.append(MIR)
#print(max_infection_rate)
#print(countries)

Task 3.3:

let's create a new Dataframe

df_MIR = pd.DataFrame()
df_MIR["Country"] = countries
df_MIR["max_infection_rate"] = max_infection_rate
df_MIR.head()

	Country	max_infection_rate
0	Afghanistan	3243.0
1	Albania	1239.0
2	Algeria	1927.0
3	Andorra	696.0
4	Angola	1493.0

Let's plot the barchart : maximum infection rate of each country

log_y changes the scale of y axis

px.bar(df_MIR,x = "Country", y = "max_infection_rate", color = "Country", title = "Global Maximum Infection Rate",log_y = "True")

Task 4: Let's See how National Lockdowns Impacts Covid19 transmission in Italy

COVID19 pandemic lockdown in Italy

On 9 March 2020, the government of Italy under Prime Minister Giuseppe Conte imposed a national quarantine, restricting the movement of the population except for necessity, work, and health circumstances, in response to the growing pandemic of COVID-19 in the country. source

italy_lockdown_start_date = '2020-03-09'
italy_lockdown_a_month_later = '2020-04-09'

df.head()  #all countries with confirmed cases > 0

	Date	Country	Confirmed	Recovered	Deaths
33	2020-02-24	Afghanistan	5	0	0
34	2020-02-25	Afghanistan	5	0	0
35	2020-02-26	Afghanistan	5	0	0
36	2020-02-27	Afghanistan	5	0	0
37	2020-02-28	Afghanistan	5	0	0

let's get data related to italy

df_italy = df[df.Country == "Italy"]

lets check the dataframe

df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths
58914	2020-01-31	Italy	2	0	0
58915	2020-02-01	Italy	2	0	0
58916	2020-02-02	Italy	2	0	0
58917	2020-02-03	Italy	2	0	0
58918	2020-02-04	Italy	2	0	0

let's calculate the infection rate in Italy

df_italy["Infection Rate"] = df_italy.Confirmed.diff()
df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate
58914	2020-01-31	Italy	2	0	0	NaN
58915	2020-02-01	Italy	2	0	0	0.0
58916	2020-02-02	Italy	2	0	0	0.0
58917	2020-02-03	Italy	2	0	0	0.0
58918	2020-02-04	Italy	2	0	0	0.0

ok! now let's do the visualization

fig = px.line(df_italy,x = "Date", y = "Infection Rate", title = "Before and after lock down in italy")

fig.add_shape(
    dict(
        type = "line",
        x0 = italy_lockdown_start_date,
        y0 = 0,
        x1 = italy_lockdown_start_date,
        y1 = df_italy["Infection Rate"].max(),
        line = dict(color = "Red", width = 2)
    )
)

fig.add_annotation(
    dict(
        x = italy_lockdown_start_date,
        y = df_italy["Infection Rate"].max(),
        text = "Starting date of lockdown in Italy"
    )
)

fig.show()

Task 5: Let's See how National Lockdowns Impacts Covid19 active cases in Italy

df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate
58914	2020-01-31	Italy	2	0	0	NaN
58915	2020-02-01	Italy	2	0	0	0.0
58916	2020-02-02	Italy	2	0	0	0.0
58917	2020-02-03	Italy	2	0	0	0.0
58918	2020-02-04	Italy	2	0	0	0.0

let's calculate number of active cases day by day

df_italy["Death Rate"] = df_italy.Deaths.diff()

let's check the dataframe again

df_italy.head()

	Date	Country	Confirmed	Recovered	Deaths	Infection Rate	Death Rate
58914	2020-01-31	Italy	2	0	0	NaN	NaN
58915	2020-02-01	Italy	2	0	0	0.0	0.0
58916	2020-02-02	Italy	2	0	0	0.0	0.0
58917	2020-02-03	Italy	2	0	0	0.0	0.0
58918	2020-02-04	Italy	2	0	0	0.0	0.0

now let's plot a line chart to compare COVID19 national lockdowns impacts on spread of the virus and number of active cases

df_italy["Infection Rate"] = df_italy["Infection Rate"]/df_italy["Infection Rate"].max()
df_italy["Death Rate"] = df_italy["Death Rate"]/df_italy["Death Rate"].max()

fig = px.line(df_italy,x = "Date", y = ["Infection Rate","Death Rate"], title = "Infection rate and death rate in Italy")
fig.show()

Folium - Geospatial Data

from IPython.display import HTML
from IPython.display import Javascript
Javascript("Jupyter.notebook.execute_cells([2])")


HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="First Click here to toggle on/off the raw code."></form>''')

import folium
from folium.plugins.measure_control import MeasureControl

#The University of Liverpool - Management School location
m = folium.Map(location=[53.40164,-2.96328], zoom_start=200)

tooltip1 = "The University of Liverpool - Management School"
tooltip2 = "Sydney Jones Library"

folium.Marker(
    [53.40164,-2.96328], popup="<i>The University of Liverpool - Management School</i>", tooltip=tooltip1
).add_to(m)

folium.Marker(
    [53.40288219796901, -2.9636515174933824], popup="<i>Sydney Jones Library</i>", tooltip=tooltip2
).add_to(m)

m

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10 Areas in England reported highest number of Omicron cases (11 Dec 2021)

https://www.express.co.uk/news/uk/1534277/omicron-covid-england-cases-evg

import folium
from folium.features import DivIcon

m1 = folium.Map(
    location=[51.37821732201087, -0.0988910151561266],
    zoom_start=11,
    #tiles='Mapbox Bright'
)

#West Northamptonshire
p1 = [52.25502059017402, -0.8989414297668287]
folium.Marker(p1, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">27</div>',
        ), popup="<i>Northamptonshire</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p1, radius=15))

#Croydon
p2 = [51.37821732201087, -0.0988910151561266]
folium.Marker(p2, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">8</div>',
        ), popup="<i>Croydon</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p2, radius=15))

#Hackney - 8
p3 = [51.57879011625016, -0.07368750825696264]
folium.Marker(p3, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">8</div>',
        ), popup="<i>Hackney</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p3, radius=15))

#Lambeth - 8
p4 = [51.46282053478865, -0.12434179492023828]
folium.Marker(p4, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">8</div>',
        ), popup="<i>Lambeth</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p4, radius=15))

#Newham - 8
p5 = [51.5277484131935, 0.03463148234397976]
folium.Marker(p5, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">8</div>',
        ), popup="<i>Newham</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p5, radius=15))


#Brent - 7
p6 = [51.57649278466461, -0.2706426347485096]
folium.Marker(p6, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">7</div>',
        ), popup="<i>Brent</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p6, radius=15))

#Buckinghamshire - 7
p7 = [51.824810372230566, -0.8108921965299508]
folium.Marker(p7, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">7</div>',
        ), popup="<i>Buckinghamshire</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p7, radius=15))


# Greenwich - 7
p8 = [51.494508660914185, 0.009608469948206529]
folium.Marker(p8, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">7</div>',
        ), popup="<i>Greenwich</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p8, radius=15))


# Lewisham - 7
p9 = [51.46182194198855, -0.007143651078944367]
folium.Marker(p9, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">7</div>',
        ), popup="<i>Lewisham</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p9, radius=15))


# Wandsworth - 6
p10 = [51.46487806381069, -0.18965168767621407]
folium.Marker(p10, icon=DivIcon(
        icon_size=(70,19),
        icon_anchor=(6,6),
        html='<div style="font-size: 9.4pt; color : black">6</div>',
        ), popup="<i>Wandsworth</i>").add_to(m1)
m1.add_child(folium.CircleMarker(p10, radius=15))

m1

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